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Upper Ontology Design for Application-Based Spatial Ontologies. Eric Little, PhD D’Youville College National Center for Ontology Research (NCOR) National Center for Multisource Information Fusion (NCMIF) Buffalo, NY USA little@dyc.edu eglittle@eng.buffalo.edu. The Structure of an Ontology.

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Upper Ontology Design for Application-Based Spatial Ontologies

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Upper ontology design for application based spatial ontologies

Upper Ontology Design for Application-Based Spatial Ontologies

Eric Little, PhD

D’Youville College

National Center for Ontology Research (NCOR)

National Center for Multisource Information Fusion (NCMIF)

Buffalo, NY USA



The structure of an ontology

The Structure of an Ontology

  • Upper-Level (Formal):

    • Most general categories of existence (e.g., existent item, spatial region, dependent part).

    • This Level of the ontology is rationally driven, meaning it is the product of philosophical reasoning.

    • Relies on a sound metaphysical description of the world (e.g., realism).

The structure of an ontology1

The Structure of an Ontology

  • Domain-Specific Level

    • Contains categories that are specific to a particular domain of interest (disaster, military/defense, medicine).

    • This level of the ontology is empirically driven, meaning it is produced by gathering expert knowledge about a given domain of interest.

    • The expert knowledge is used to create a consistent and comprehensive lexicon of terms.

Synthesized ontology model

Synthesized Ontology Model

Ontologies vs taxonomies

Ontologies vs. Taxonomies

Urban Environment


IED Taxonomy

Dirty Bomb



Taxonomy A

Taxonomy B

Taxonomy C


Using knowledge representation reasoning krr to conjoin taxonomies

SPAN Taxonomy (Temporal Items)

SNAP Taxonomy (Spatial Items)

Using Knowledge Representation & Reasoning (KRR) to Conjoin Taxonomies

Transcategorical Relations

Represented in KRR


An Intentional Act is a Psychological Act

that depends on an agent to instantiate it.

It stands in a relation of dependence to

other items such as neuro-biological


Relating ontology to other engineering practices

Relating Ontology to Other Engineering Practices

  • Ontologies informthe design of other engineering systems (e.g., agent-based sys, decision support sys, predictive analytics, etc.) by providing a structured comprehensive picture of their domains.

    • Many engineering practices require a more principled basis for their design.

  • Engineering systems constrain the ontology by providing inputs such as:

    • User needs

    • Domain specificity

    • Computational tractability

      • If you give philosophers carte blanche, remember … fools and their $ are easily parted.

Higher level fusion

Higher Level Fusion

The purpose of higher level fusion is to develop probable explanations of a situation based on prior knowledge and incoming transient information to produce a coherent composite picture of the current situation along with a prediction of consequences.

A dynamic situational picture is the result of reasoning about objects, attributes, aggregates, relationships and their behavior over time within a specific context.

The process of building the dynamic situational picture requires formally structured and computationally tractable domain representation.

What kinds of ontologies are needed for high level fusion sta

What kinds of ontologies are needed for High-level fusion & STA?

  • Low – level fusion can be done (to a large degree) using existing tools such as OWL, Protégé, DAML - Oil, etc.

  • However, higher-level fusion processing is concerned with providing comprehensive and consistent descriptions of highly complex world states.

  • Hence we need a more “industrial strength” (cf. Musen) approach than is provided by current fusion ontologies.

Relations between situational objects at different levels of granularity

Relations Between Situational Objects at Different Levels of Granularity

  • Inter - Relationships: 1) Relationships between situational items of different types. 2) Relationships between items and aggregates of items of a different type. 3) relationships between aggregates of objects of different types

  • Intra - Relationships: 1) Relationships between different physical objects or their respective attributes/properties. 2) Relationships between different clusters/aggregates of objects in the same group.

Physical objects –

Physical objects (PO-PO)

Combinations of ES (CES) –

Combinations of ES





PO- Aggregates of PO

Elementary Situation

-Elementary situation



Aggregates- Aggregates



(process aggregation)


(event aggregation)

Ontologize this

Ontologize this…

Frank White (Workshop II on Ontologies and Higher-lvl Fusion – Beaver Hollow

Existing fusion ontology models often confuse various kinds of relations

Existing fusion ontology models often confuse various kinds of relations

Temporal Relations

Spatial Relations

Situation Awareness (SAW) Ontology Model for Battlefield Relations

(C. J. Matheus, M. M. Kokar, and K. Baclawski. (2003)

It gets worse

It gets worse…

Complex Relation


Examples of important relationships

Relationships between time points

Before, At the same time, Start, Finish, Soon, Very soon, Resulting in, Initiating

Relationships between time intervals

Disjoint, Joint, Overlap, Inside, Equal

SNAP relations





















A part of


Very far


Very near

Small (er)



Disaster Examples:

“Close to a hospital”

“Cluster A is larger than before”

“Along the wind direction”

“Distance between Clusters A and B is smaller than before”

“Casualty cluster A overlaps with building cluster C”

Examples of Important Relationships

SPAN relations

Building reasoning processes with ontologies

  • SNAP

  • Ontology

  • Spatial Items

  • Of Interest

  • SPAN

  • Ontology

  • Temporal Items

  • Of Interest

Building Reasoning Processes with Ontologies


Reasoning about

relations represented

in Ontology



(Objects + Processes)

Segment of snap kharkiv nuclear facility ontology

Segment of SNAP Kharkiv Nuclear Facility Ontology

Segment of span ontology for kharkiv nuclear facility

Segment of SPAN ontology for Kharkiv Nuclear Facility

Small representative sample of the snap dis reo ontology w cwa

Small Representative Sample of the SNAP Dis-ReO Ontology w/ CWA

Bisantz, A., Rogova, G., Little, E. (2004) “On the Integration of Cognitive Work Analysis within a

Multisource Information Fusion Development Methodology,” Proceedings of the Human Factors and

Ergonomics Society Annual Meeting, New Orleans

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